Guo Pengfei, Li Dawei, Li Xingde
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Equal contribution.
Biomed Opt Express. 2020 Jun 8;11(7):3543-3554. doi: 10.1364/BOE.392882. eCollection 2020 Jul 1.
We report an end-to-end image compression framework for retina optical coherence tomography (OCT) images based on convolutional neural networks (CNNs), which achieved an image size compression ratio as high as 80. Our compression scheme consists of three parts: data preprocessing, compression CNNs, and reconstruction CNNs. The preprocessing module was designed to reduce OCT speckle noise and segment out the region of interest. Skip connections with quantization were developed and added between the compression CNNs and the reconstruction CNNs to reserve the fine-structure information. Two networks were trained together by taking the semantic segmented images from the preprocessing module as input. To train the two networks sensitive to both low and high frequency information, we leveraged an objective function with two components: an adversarial discriminator to judge the high frequency information and a differentiable multi-scale structural similarity (MS-SSIM) penalty to evaluate the low frequency information. The proposed framework was trained and evaluated on ophthalmic OCT images with pathological information. The evaluation showed reconstructed images can still achieve above 99% similarity in terms of MS-SSIM when the compression ratio reached 40. Furthermore, the reconstructed images after 80-fold compression with the proposed framework even presented comparable quality with those of a compression ratio 20 from state-of-the-art methods. The test results showed that the proposed framework outperformed other methods in terms of both MS-SSIM and visualization, which was more obvious at higher compression ratios. Compression and reconstruction were fast and took only about 0.015 seconds per image. The results suggested a promising potential of deep neural networks on customized medical image compression, particularly valuable for effective image storage and tele-transfer.
我们报告了一种基于卷积神经网络（CNN）的用于视网膜光学相干断层扫描（OCT）图像的端到端图像压缩框架，该框架实现了高达80的图像尺寸压缩率。我们的压缩方案由三部分组成：数据预处理、压缩CNN和重建CNN。预处理模块旨在减少OCT散斑噪声并分割出感兴趣区域。在压缩CNN和重建CNN之间开发并添加了带量化的跳跃连接，以保留精细结构信息。通过将预处理模块的语义分割图像作为输入，一起训练两个网络。为了训练对低频和高频信息都敏感的两个网络，我们利用了一个具有两个组件的目标函数：一个对抗判别器来判断高频信息，以及一个可微多尺度结构相似性（MS-SSIM）惩罚来评估低频信息。所提出的框架在具有病理信息的眼科OCT图像上进行了训练和评估。评估表明，当压缩率达到40时，重建图像在MS-SSIM方面仍能达到99%以上的相似度。此外，使用所提出的框架进行80倍压缩后的重建图像，其质量甚至与最先进方法压缩率为20时的图像相当。测试结果表明，所提出的框架在MS-SSIM和可视化方面均优于其他方法，在更高压缩率下更为明显。压缩和重建速度很快，每张图像仅需约0.015秒。结果表明，深度神经网络在定制医学图像压缩方面具有广阔的潜力，对有效的图像存储和远程传输特别有价值。
